NCT07263204

AI-Enabled Diagnosis and Prognosis of Hypertrophic Cardiomyopathy

Precision Diagnosis and Prognostic Prediction of Hypertrophic Cardiomyopathy Using Artificial Intelligence: A Multicenter Study

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 15,000 (estimated)
• Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

By harnessing artificial intelligence to decode the 12-lead electrocardiogram, the project will enable precise ECG-based phenotyping of hypertrophic cardiomyopathy-accurately classifying septal, apical, and other morphologic subtypes-while simultaneously differentiating HCM from hypertensive heart disease, aortic stenosis, and other phenocopy disorders.

Detailed description

To overcome the twin bottlenecks of late detection and poor inter-centre reproducibility, the project leverages a large, multicentre historical cohort and anchors its pipeline on the 12-lead ECG-an inexpensive, ubiquitously available signal that can be captured in any department. Using deep-learning architectures augmented with attention mechanisms, we will develop (1) a discriminative model that separates HCM from phenocopies and normal hearts, and (2) an algorithmic framework that remains stable across devices and populations. Model governance will be embedded through version-controlled releases, cloud-edge deployment, and an "offline replay" evaluation loop, producing an end-to-end evidence chain that mirrors real-world clinical workflows.

Conditions

• Hypertrophic Cardiomyopathy (HCM)
• Left Ventricular Hypertrophy

Timeline

• Start date: 2025-01-01
• Primary completion: 2026-06-01
• Completion: 2026-12-31
• First posted: 2025-12-04
• Last updated: 2025-12-04

Locations

1 site across 1 country: China